Tesis sobre el tema "Bio-statistique"
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Douib, Ameur. "Algorithmes bio-inspirés pour la traduction automatique statistique". Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0005/document.
Texto completoDifferent components of statistical machine translation systems are considered as optimization problems. Indeed, the learning of the translation model, the decoding and the optimization of the weights of the log-linear function are three important optimization problems. Knowing how to define the right algorithms to solve them is one of the most important tasks in order to build an efficient translation system. Several optimization algorithms are proposed to deal with decoder optimization problems. They are combined to solve, on the one hand, the decoding problem that produces a translation in the target language for each source sentence, on the other hand, to solve the problem of optimizing the weights of the combined scores in the log-linear function to fix the translation evaluation function during the decoding. The reference system in statistical translation is based on a beam-search algorithm for the decoding, and a line search algorithm for optimizing the weights associated to the scores. We propose a new statistical translation system with a decoder entirely based on genetic algorithms. Genetic algorithms are bio-inspired optimization algorithms that simulate the natural process of evolution of species. They allow to handle a set of solutions through several iterations to converge towards optimal solutions. This work allows us to study the efficiency of the genetic algorithms for machine translation. The originality of our work is the proposition of two algorithms: a genetic algorithm, called GAMaT, as a decoder for a phrase-based machine translation system, and a second genetic algorithm, called GAWO, for optimizing the weights of the log-linear function in order to use it as a fitness function for GAMaT. We propose also, a neuronal approach to define a new fitness function for GAMaT. This approach consists in using a neural network to learn a function that combines several scores, which evaluate different aspects of a translation hypothesis, previously combined in the log-linear function, and that predicts the BLEU score of this translation hypothesis. This work allowed us to propose a new machine translation system with a decoder entirely based on genetic algorithms
Douib, Ameur. "Algorithmes bio-inspirés pour la traduction automatique statistique". Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0005.
Texto completoDifferent components of statistical machine translation systems are considered as optimization problems. Indeed, the learning of the translation model, the decoding and the optimization of the weights of the log-linear function are three important optimization problems. Knowing how to define the right algorithms to solve them is one of the most important tasks in order to build an efficient translation system. Several optimization algorithms are proposed to deal with decoder optimization problems. They are combined to solve, on the one hand, the decoding problem that produces a translation in the target language for each source sentence, on the other hand, to solve the problem of optimizing the weights of the combined scores in the log-linear function to fix the translation evaluation function during the decoding. The reference system in statistical translation is based on a beam-search algorithm for the decoding, and a line search algorithm for optimizing the weights associated to the scores. We propose a new statistical translation system with a decoder entirely based on genetic algorithms. Genetic algorithms are bio-inspired optimization algorithms that simulate the natural process of evolution of species. They allow to handle a set of solutions through several iterations to converge towards optimal solutions. This work allows us to study the efficiency of the genetic algorithms for machine translation. The originality of our work is the proposition of two algorithms: a genetic algorithm, called GAMaT, as a decoder for a phrase-based machine translation system, and a second genetic algorithm, called GAWO, for optimizing the weights of the log-linear function in order to use it as a fitness function for GAMaT. We propose also, a neuronal approach to define a new fitness function for GAMaT. This approach consists in using a neural network to learn a function that combines several scores, which evaluate different aspects of a translation hypothesis, previously combined in the log-linear function, and that predicts the BLEU score of this translation hypothesis. This work allowed us to propose a new machine translation system with a decoder entirely based on genetic algorithms
Dortel, Emmanuelle. "Croissance de l'albacore (Thunnus albacares) de l'Océan Indien : de la modélisation statistique à la modélisation bio-énergétique". Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20035/document.
Texto completoSince the early 1960s, the growth of yellowfin has been enjoyed a particular attention both in the research field and for fisheries management. In the Indian Ocean, the management of yellowfin stock, under the jurisdiction of the Indian Ocean Tuna Commission (IOTC), suffers from much uncertainty associated with the growth curve currently considered. In particular, there remain gaps in our knowledge of basic biological and ecological processes regulating growth. Their knowledge is however vital for understanding the stocks productivity and their resilience abilities to fishing pressure and oceanographic changes underway.Through modelling, this study aims to improve current knowledge on the growth of yellowfin population of the Indian Ocean and thus strengthen the scientific advice on the stock status. Whilst most studies on yellowfin growth only rely on one data source, we implemented a hierarchical Bayesian model that exploits various information sources on growth, i.e. direct age estimates obtained through otolith readings, analyzes of modal progressions and individual growth rates derived from mark-recapture experiments, and takes explicitely into account the expert knowledge and the errors associated with each dataset and the growth modelling process. In particular, the growth model was coupled with an ageing error model from repeated otolith readings which significantly improves the age estimates as well as the resulting growth estimates and allows a better assessment of the estimates reliability. The growth curves obtained constitute a major improvement of the growth pattern currently used in the yellowfin stock assessment. They demonstrates that yellowfin exhibits a two-stanzas growth, characterized by a sharp acceleration at the end of juvenile stage. However, they do not provide information on the biological and ecological mechanisms that lie behind the growth acceleration.For a better understanding of factors involved in the acceleration of growth, we implemented a bioenergetic model relying on the principles of Dynamic Energy Budget theory (DEB). Two major assumptions were investigated : (i) a low food availability during juvenile stage in relation with high intra and inter-specific competition and (ii) changes in food diet characterized by the consumption of more energetic prey in older yellowfin. It appears that these two assumption may partially explain the growth acceleration
Hadj, Amor Khaoula. "Classification et inférence de réseaux de gènes à partir de séries temporelles très courtes : application à la modélisation de la mémoire transcriptionnelle végétale associée à des stimulations sonores répétées". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES037.
Texto completoAdvancements in new sequencing technologies have paved the way for accessing dynamic gene expression data on a genome-wide scale. Classical ensemble approaches traditionally used in biology fall short of comprehending the underlying the complex molecular mechanisms. Consequently, developing analytical methods to understand further such data poses a significant challenge for current biology. However, the technical and experimental costs associated with transcriptomic data severely limit the dimension of real datasets and their analytical methods. Throughout my thesis, at the intersection of applied mathematics and plant biology, I focused on implementing an inference method for dynamic regulatory networks tailored to a real and original dataset describing the effect of repeated acoustic stimulations on genes expressions of Arabidopsis thaliana. I proposed a clustering method adapted to very-short time series that groups genes based on temporal variations, adjusting the data dimension for network inference. The comparison of this method with classical methods showed that it was the most suitable for very-short time series with irregular time points. For the network inference, I proposed a model that takes into account intra-class variability and integrates a constant term explicitly describing the external stimulation of the system. The evaluation of these classification and inference methods was conducted on simulated and real-time series data, which established their high performance in terms of accuracy, recall, and prediction error. The implementation of these methods to study the priming of the immune response of Arabidopsis thaliana through repeated sound waves. We demonstrated the formation of a transcriptional memory associated with stimulations, transitioning the plant from a naïve state to a primed and more resistant state within 3 days. This resistant state, maintained by stimulations and transcription factor cascades, enhances the plant's immune resistance by triggering the expression of resistance genes in healthy plants, diversifying the number of genes involved in the immune response, and intensifying the expression of numerous resistance genes. The inference of the network describing the transcriptional memory associated with repeated sound stimulations allowed us to identify the properties conferred to plants. Experimentally validated predictions showed that increasing the frequency between stimulations does not result in a more significant resistance gain, and the transcriptional memory lasts only 1.5 days after the last stimulation
Playe, Benoit. "Méthodes d'apprentissage statistique pour le criblage virtuel de médicament". Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM010/document.
Texto completoThe rational drug discovery process has limited success despite all the advances in understanding diseases, and technological breakthroughs. Indeed, the process of drug development is currently estimated to require about 1.8 billion US dollars over about 13 years on average. Computational approaches are promising ways to facilitate the tedious task of drug discovery. We focus in this thesis on statistical approaches which virtually screen a large set of compounds against a large set of proteins, which can help to identify drug candidates for known therapeutic targets, anticipate potential side effects or to suggest new therapeutic indications of known drugs. This thesis is conceived following two lines of approaches to perform drug virtual screening : data-blinded feature-based approaches (in which molecules and proteins are numerically described based on experts' knowledge), and data-driven feature-based approaches (in which compounds and proteins numerical descriptors are learned automatically from the chemical graph and the protein sequence). We discuss these approaches, and also propose applications of virtual screening to guide the drug discovery process
Massé, Pierre-Yves. "Autour De L'Usage des gradients en apprentissage statistique". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS568/document.
Texto completoWe prove a local convergence theorem for the classical dynamical system optimization algorithm called RTRL, in a nonlinear setting. The rtrl works on line, but maintains a huge amount of information, which makes it unfit to train even moderately big learning models. The NBT algorithm turns it by replacing these informations by a non-biased, low dimension, random approximation. We also prove the convergence with arbitrarily close to one probability, of this algorithm to the local optimum reached by the RTRL algorithm. We also formalize the LLR algorithm and conduct experiments on it, on synthetic data. This algorithm updates in an adaptive fashion the step size of a gradient descent, by conducting a gradient descent on this very step size. It therefore partially solves the issue of the numerical choice of a step size in a gradient descent. This choice influences strongly the descent and must otherwise be hand-picked by the user, following a potentially long research
Blum, Michael G. B. "Statistique bayésienne et applications en génétique des populations". Habilitation à diriger des recherches, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00766196.
Texto completoRamstein, Gérard. "Application de techniques de fouille de données en Bio-informatique". Habilitation à diriger des recherches, Université de Nantes, 2012. http://tel.archives-ouvertes.fr/tel-00706566.
Texto completoMoulin, Serge. "Use of data analysis techniques to solve specific bioinformatics problems". Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD049/document.
Texto completoNowadays, the quantity of sequenced genetic data is increasing exponentially under the impetus of increasingly powerful sequencing tools, such as high-throughput sequencing tools in particular. In addition, these data are increasingly accessible through online databases. This greater availability of data opens up new areas of study that require statisticians and bioinformaticians to develop appropriate tools. In addition, constant statistical progress in areas such as clustering, dimensionality reduction, regressions and others needs to be regularly adapted to the context of bioinformatics. The objective of this thesis is the application of advanced statistical techniques to bioinformatics issues. In this manuscript we present the results of our works concerning the clustering of genetic sequences via Laplacian eigenmaps and Gaussian mixture model, the study of the propagation of transposable elements in the genome via a branching process, the analysis of metagenomic data in ecology via ROC curves or the ordinal polytomous regression penalized by the l1-norm
Belkhir, Nacim. "Per Instance Algorithm Configuration for Continuous Black Box Optimization". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS455/document.
Texto completoThis PhD thesis focuses on the automated algorithm configuration that aims at finding the best parameter setting for a given problem or a' class of problem. The Algorithm Configuration problem thus amounts to a metal Foptimization problem in the space of parameters, whosemetaFobjective is the performance measure of the given algorithm at hand with a given parameter configuration. However, in the continuous domain, such method can only be empirically assessed at the cost of running the algorithm on some problem instances. More recent approaches rely on a description of problems in some features space, and try to learn a mapping from this feature space onto the space of parameter configurations of the algorithm at hand. Along these lines, this PhD thesis focuses on the Per Instance Algorithm Configuration (PIAC) for solving continuous black boxoptimization problems, where only a limited budget confessionnalisations available. We first survey Evolutionary Algorithms for continuous optimization, with a focus on two algorithms that we have used as target algorithm for PIAC, DE and CMAFES. Next, we review the state of the art of Algorithm Configuration approaches, and the different features that have been proposed in the literature to describe continuous black box optimization problems. We then introduce a general methodology to empirically study PIAC for the continuous domain, so that all the components of PIAC can be explored in real Fworld conditions. To this end, we also introduce a new continuous black box test bench, distinct from the famous BBOB'benchmark, that is composed of a several multiFdimensional test functions with different problem properties, gathered from the literature. The methodology is finally applied to two EAS. First we use Differential Evolution as'target algorithm, and explore all the components of PIAC, such that we empirically assess the best. Second, based on the results on DE, we empirically investigate PIAC with Covariance Matrix Adaptation Evolution Strategy (CMAFES) as target algorithm. Both use cases empirically validate the proposed methodology on the new black box testbench for dimensions up to100
Posani, Lorenzo. "Inference and modeling of biological networks : a statistical-physics approach to neural attractors and protein fitness landscapes". Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE043/document.
Texto completoThe recent advent of high-throughput experimental procedures has opened a new era for the quantitative study of biological systems. Today, electrophysiology recordings and calcium imaging allow for the in vivo simultaneous recording of hundreds to thousands of neurons. In parallel, thanks to automated sequencing procedures, the libraries of known functional proteins expanded from thousands to millions in just a few years. This current abundance of biological data opens a new series of challenges for theoreticians. Accurate and transparent analysis methods are needed to process this massive amount of raw data into meaningful observables. Concurrently, the simultaneous observation of a large number of interacting units enables the development and validation of theoretical models aimed at the mechanistic understanding of the collective behavior of biological systems. In this manuscript, we propose an approach to both these challenges based on methods and models from statistical physics. We present an application of these methods to problems from neuroscience and bioinformatics, focusing on (1) the spatial memory and navigation task in the hippocampal loop and (2) the reconstruction of the fitness landscape of proteins from homologous sequence data
Luu, Keurcien. "Application de l'Analyse en Composantes Principales pour étudier l'adaptation biologique en génomique des populations". Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAS053/document.
Texto completoIdentifying genes involved in local adaptation is of major interest in population genetics. Current statistical methods for genome scans are no longer suited to the analysis of Next Generation Sequencing (NGS) data. We propose new statistical methods to perform genome scans on massive datasets. Our methods rely exclusively on Principal Component Analysis which use in population genetics will be discussed extensively. We also explain the reasons why our approaches can be seen as extensions of existing methods and demonstrate how our PCA-based statistics compare with state-of-the-art methods. Our work has led to the development of pcadapt, an R package designed for outlier detection for various genetic data
Jung, Matthieu. "Évolution du VIH : méthodes, modèles et algorithmes". Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2012. http://tel.archives-ouvertes.fr/tel-00842785.
Texto completoGuindon, Stephane. "Méthodes et algorithmes pour l'approche statistique en phylogénie". Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2003. http://tel.archives-ouvertes.fr/tel-00843343.
Texto completoCharafeddine, Jinan. "Caractérisation et intégration des signaux musculaires pour le pilotage d'un exosquelette des membres inférieurs lors d' activités locomotrices". Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPASW005.
Texto completoDaily activities are a source of fatigue and stress for people with lower limb spasticity. A better understanding of the mechanisms of movement thus makes it possible to propose innovative models for control-ling assistance exoskeletons. The assistance should be introduced while retaining the fact that patient control remains a priority. This thesis aims to develop such an application in the context of walking on the exoskeleton developed at the Laboratory of Systems Engineering of Versailles (LISV). The application results of this thesis are based on the database recorded at the END-ICAP laboratory with gait sensors for healthy subjects, PC people, and people with stroke. The main contribution is the proposal of a new method of neuromotor control of an interactive exoskeleton allowing the necessary rehabilitation of the members. It consists of determining and supplementing the motor instructions related to a patient’s movement while retaining his expertise in his movement, assisting when necessary, and detecting his intention to cause this movement from a fusion of information. The results obtained show that the proposed index characterizes the relationship of the angle difference with a reference movement for each joint, which allows a dynamic compensation of the movements in an efficient and safe manner, applicable for gait pathology studies, and for walking control in robotic assistance for people
Jauffret, Adrien. "De l'auto-évaluation aux émotions : approche neuromimétique et bayésienne de l'apprentissage de comportements complexes impliquant des informations multimodales". Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112120/document.
Texto completoThe goal of this thesis is to build a bio-inspired architecture allowing a robot to autonomouslynavigate over large distances. In a cognitive science point of view, the model also aim at improv-ing the understanding of the underlying biological mechanisms. Previous works showed thata computational model of hippocampal place cells, based on neurobiological studies made onrodent, allows a robot to learn robust navigation behaviors. The robot can learn a round or ahoming behavior from a few associations between places and actions. The learning and recog-nition of a place were only defined by visual information and shows limitations for navigatinglarge environments.Adding other sensorial modalities is an effective solution for improving the robustness of placesrecognition in complex environments. This solution led us to the elementary blocks requiredwhen trying to perform multimodal information merging. Such merging has been done, first,by a simple conditioning between 2 modalities and next improved by a more generic model ofinter-modal prediction. In this model, each modality learns to predict the others in usual situa-tions, in order to be able to detect abnormal situations and to compensate missing informationof the others. Such a low level mechanism allows to keep a coherent perception even if onemodality is wrong. Moreover, the model can detect unexpected situations and thus exhibit someself-assessment capabilities: the assessment of its own perception. Following this model of self-assessment, we focus on the fundamental properties of a system for evaluating its behaviors.The first fundamental property that pops out is the statement that evaluating a behavior is anability to recognize a dynamics between sensations and actions, rather than recognizing a sim-ple sensorial pattern. A first step was thus to take into account the sensation/action couplingand build an internal minimalist model of the interaction between the agent and its environment.Such of model defines the basis on which the system will build predictions and expectations.The second fundamental property of self-assessment is the ability to extract relevant informa-tion by the use of statistical processes to perform predictions. We show how a neural networkcan estimate probability density functions through a simple conditioning rule. This probabilis-tic learning allows to achieve bayesian inferences since the system estimates the probability ofobserving a particular behavior from statistical information it recognizes about this behavior.The robot estimates the different statistical momentums (mean, variance, skewness, etc...) of abehavior dynamics by cascading few simple conditioning. Then, the non-recognition of such adynamics is interpreted as an abnormal behavior.But detecting an abnormal behavior is not sufficient to conclude to its inefficiency. The systemmust also monitor the temporal evolution of such an abnormality to judge the relevance of thebehavior. We show how an emotional meta-controller can use this novelty detection to regu-late behaviors and so select the best appropriate strategy in a given context. Finally, we showhow a simple frustration mechanism allows the robot to call for help when it detects potentialdeadlocks. Such a mechanism highlights situations where a skills improvement is possible, soas some developmental processes
Xayaphoummine, Alain. "Simulations et expériences sur le repliement de l'ARN : prédictions statistiques des pseudonoeuds in silico et réalisation de commutateurs ARN par transcription in vitro". Phd thesis, Université Louis Pasteur - Strasbourg I, 2004. http://tel.archives-ouvertes.fr/tel-00221533.
Texto completoDéveloppement d'un algorithme d'accélération exacte de la dynamique de monte Carlo.
Développement et interfaçage web d'un code de repliement de molécule ARN incluant les motifs pseudoneuds. Rendu cinématique de la dynamique de repliement.
Etude statistique de la prévalence des pseudoneouds dans des séquences biologiques et aléatoires.
Vérification expérimentale du code repliement. Démonstration expérimentale de l'existence d'un super-code guidant pour le repliement natif des ARN.
Causo, Matteo. "Neuro-Inspired Energy-Efficient Computing Platforms". Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10004/document.
Texto completoBig Data highlights all the flaws of the conventional computing paradigm. Neuro-Inspired computing and other data-centric paradigms rather address Big Data to as resources to progress. In this dissertation, we adopt Hierarchical Temporal Memory (HTM) principles and theory as neuroscientific references and we elaborate on how Bayesian Machine Learning (BML) leads apparently totally different Neuro-Inspired approaches to unify and meet our main objectives: (i) simplifying and enhancing BML algorithms and (ii) approaching Neuro-Inspired computing with an Ultra-Low-Power prospective. In this way, we aim to bring intelligence close to data sources and to popularize BML over strictly constrained electronics such as portable, wearable and implantable devices. Nevertheless, BML algorithms demand for optimizations. In fact, their naïve HW implementation results neither effective nor feasible because of the required memory, computing power and overall complexity. We propose a less complex on-line, distributed nonparametric algorithm and show better results with respect to the state-of-the-art solutions. In fact, we gain two orders of magnitude in complexity reduction with only algorithm level considerations and manipulations. A further order of magnitude in complexity reduction results through traditional HW optimization techniques. In particular, we conceive a proof-of-concept on a FPGA platform for real-time stream analytics. Finally, we demonstrate we are able to summarize the ultimate findings in Machine Learning into a generally valid algorithm that can be implemented in HW and optimized for strictly constrained applications
Boukinda, Mbadinga Morgan Laetitia. "Surface de réponse des efforts de houle des structures jackets colonisées par des bio-salissures". Nantes, 2007. http://www.theses.fr/2007NANT2158.
Texto completoWhen designing or re-assessing an offshore structure, one of the most delicate stages relates to the calculation of the solicitations : actions exerted by the swell, the wind and the currents. It comes partly from the randomness or uncertainties that concern the marine environment as well as the modelling of loading. Presence of marine growth makes these questions more sensitive. The generic term of marine growth includes the vegetable species (algae …) and animal (mussel, anemones, corals …). Indeed very quickly, the structures are covered of a multitude of marine organisms. It remains particularly difficult to quantify this phenomenon by taking into account the diversity of the organism, the seasonal conditions and the competition to which the various species for their survival are delivered. Its influence on an offshore structure can be measured on several levels : obstruct or prohibits a visual inspection of the subjacent support, expensive procedures of cleaning for oil industries, increase in the hydrodynamic efforts on the level of the structure. This work aims to provide a probabilistic modelling of marine growth evolution in five regions in the Gulf of Guinea. A physical matrix response surface is then built in view to provide a probabilistic modelling of the environmental loading on Jacket offshore structures in presence of marine growth. A study case allows performing sensitivity and uncertainty studies in view to improve, supplement, integrate and make more operational the methods and tools for structural reassessment
Benavides, Parra Juan Carlos. "Brownian motion of colloidal particles located near different types of interfaces". Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1011.
Texto completoWe explore the Brownian motion of colloids near different interfaces (water-air, water solid,…) using three dimensional digital video microscopy and reconstruction of singles colloids trajectories in 3D over time. Satisfying agreements between data and published theoretical models were found for simplest cases. In addition we propose a theoretical approach able to transit from the free interface configuration (water-air) to the bound condition (liquid-solid). We also considered within this frame the situation where a solid interface was functionalized with a grafted short alkyl chain (flat and hydrophobic fixed wall) to compare with same solid interface made hydrophilic from a UV-ozone plasma treatment that creates hydroxyl groups (Si-OH). From the stabilization of a phospholipid bilayer, we also studied colloidal and hydrodynamic interaction with a soft (DOPC in Lα) or freezed (DMPC at Lβ) biomimetic membrane covering the solid interface (SiO2 glass)
Brouard, Céline. "Inférence de réseaux d'interaction protéine-protéine par apprentissage statistique". Phd thesis, Université d'Evry-Val d'Essonne, 2013. http://tel.archives-ouvertes.fr/tel-00845692.
Texto completoMarzin, Anahita. "Indicateurs biologiques de la qualité écologique des cours d’eau : variabilités et incertitudes associées". Thesis, Paris, AgroParisTech, 2013. http://www.theses.fr/2013AGPT0002/document.
Texto completoSensitive biological measures of ecosystem quality are needed to assess, maintain or restore the ecological conditions of rivers. Since our understanding of these complex systems is imperfect, river management requires recognizing variability and uncertainty of bio-assessment for decision-making. Based on the analysis of national data sets (~ 1654 sites), the main goals of this work were (1) to test some of the assumptions that shape bio-indicators and (2) address the temporal variability and the uncertainty associated to prediction of reference conditions.(1) This thesis highlights (i) the predominant role of physiographic factors in shaping biological communities in comparison to human pressures (defined at catchment, riparian corridor and reach scales), (ii) the differences in the responses of biological indicators to the different types of human pressures (water quality, hydrological, morphological degradations) and (iii) more generally, the greatest biological impacts of water quality alterations and impoundments. (2) A Bayesian method was developed to estimate the uncertainty associated with reference condition predictions of a fish-based bio-indicator (IPR+). IPR+ predictive uncertainty was site-dependent but showed no clear trend related to the environmental gradient. By comparison, IPR+ temporal variability was lower and sensitive to an increase of human pressure intensity. This work confirmed the advantages of multi-metric indexes based on functional metrics in comparison to compositional metrics. The different sensitivities of macrophytes, fish, diatoms and macroinvertebrates to human pressures emphasize their complementarity in assessing river ecosystems. Nevertheless, future research is needed to better understand the effects of interactions between pressures and between pressures and the environment
Sun, Shengnan. "Modeling and mechanical characterization of a bio-sourced composite by non-contact kinematic field measurements". Thesis, Clermont-Ferrand 2, 2014. http://www.theses.fr/2014CLF22450/document.
Texto completoThis thesis was carried out within the framework of the project Demether started in 2011. The objective of this project is to develop a bio-based composite material for thermal insulation of existing buildings. These biocomposites consist of shredded sunflower stems linked by a chitosan-based biomatrix. My work is mainly focused on the characterization and the modeling of the mechanical properties of both the sunflower stem and the biocomposite. The first part of this work highlighted the influence of both the specimen sampling location and the conditioning relative humidity on the Young's modulus of sunflower stem. A statistical approach enabled us to take into account the diffuse nature of the stems on their mechanical properties. Thereafter, a homogenization work was carried out. It led to an estimate of the elastic property of the bark based on the morphology and the characteristics of the constituents. In the second phase of the work, the mechanical behavior of the biocomposite under compression was characterized by applying a full-field measurement technique. The heterogeneous nature of the deformation fields was directly linked to the constituents and the chitosan mass percentage of the biocomposite
Luhandjula, Thierry. "Algorithme de reconnaissance visuelle d’intentions : application au pilotage automatique d’un fauteuil roulant". Thesis, Paris Est, 2012. http://www.theses.fr/2012PEST1092/document.
Texto completoIn this thesis, a methodological and algorithmic approach is proposed, for visual intention recognition based on the rotation and the vertical motion of the head and the hand. The context for which this solution is intended is that of people with disabilities whose mobility is made possible by a wheelchair. The proposed system is an interesting alternative to classical interfaces such as joysticks and pneumatic switches. The video sequence comprising 10 frames is processed using different methods leading to the construction of what is referred to in this thesis as an “intention curve”. A decision rule is proposed to subsequently classify each intention curve. For recognition based on head motions, a symmetry-based approach is proposed to estimate the direction intent indicated by a rotation and a Principal Component Analysis (PCA) is used to classify speed variation intents of the wheelchair indicated by a vertical motion. For recognition of the desired direction based on the rotation of the hand, an approach utilizing both a vertical symmetry-based approach and a machine learning algorithm (a neural network, a support vector machine or k-means clustering) results in a set of two intention curves subsequently used to detect the direction intent. Another approach based on the template matching of the finger region is also proposed. For recognition of the desired speed variation based on the vertical motion of the hand, two approaches are proposed. The first is also based on the template matching of the finger region, and the second is based on a mask in the shape of an ellipse used to estimate the vertical position of the hand. The results obtained display good performance in terms of classification both for single pose in each frame and for intention curves. The proposed visual intention recognition approach yields in the majority of cases a better recognition rate than most of the methods proposed in the literature. Moreover, this study shows that the head and the hand in rotation and in vertical motion are viable intent indicators
Sun, Shengnan. "Modélisation et caractérisation mécanique d'un composite bio-sourcé par mesures de champs cinématiques sans contact". Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2014. http://tel.archives-ouvertes.fr/tel-01020039.
Texto completoMarzin, Anahita. "Indicateurs biologiques de la qualité écologique des cours d'eau : variabilités et incertitudes associées". Phd thesis, AgroParisTech, 2013. http://pastel.archives-ouvertes.fr/pastel-00879788.
Texto completoKleinman, Claudia L. "Statistical potentials for evolutionary studies". Thèse, 2010. http://hdl.handle.net/1866/5185.
Texto completoProtein sequences are the net result of the interplay of mutation, natural selection and stochastic variation. Probabilistic models of molecular evolution accounting for these processes have been substantially improved over the last years. In particular, models that explicitly incorporate protein structure and site interdependencies have recently been developed, as well as statistical tools for assessing their performance. Despite major advances in this direction, only simple representations of protein structure have been used so far. In this context, the main theme of this dissertation has been the modeling of three-dimensional protein structure for evolutionary studies, taking into account the limitations imposed by computationally demanding phylogenetic methods. First, a general statistical framework for optimizing the parameters of a statistical potential (an energy-like scoring system for sequence-structure compatibility) is presented. The functional form of the potential is then refined, increasing the detail of structural description without inflating computational costs. Always at the residue-level, several structural elements are investigated: pairwise distance interactions, solvent accessibility, backbone conformation and flexibility of the residues. The potentials are then included into an evolutionary model and their performance is assessed in terms of model fit, compared to standard evolutionary models. Finally, this new structurally constrained phylogenetic model is used to better understand the selective forces behind the differences in conservation found in genes of very different expression levels.
Bueno, Virginie. "Inclure l’addiction à Internet dans le DSM-V : étude de cas de la biomédicalisation des cyberdépendances". Thèse, 2014. http://hdl.handle.net/1866/11538.
Texto completoThe proposal of "Internet Addiction" in the fifth edition of the DSM : a case study of biomedicalisation : The representation of Internet excessive practices as an addiction is a highly criticized fact in the scientific field. The proposed inclusion of the mental disorder "Internet Addiction" in the fifth version of the Diagnostic and Statistical Manuel, has ended in March 2013 in the inclusion of the "Internet Gaming Disorder" in the Appendix section caracterised by the need to further research. The aim of this master thesis is to understand the processes which get through the debate over this inclusion. Therefore, from a socio-historical perspective, the analysis first exposed the biomedicalized process that create the pathology. Then, empirically, through discourse analysis, that process is systematized in order to understand the representation of the "internet addict" that emerge from these scientific discourse. Finally, we suggest that this pathology reflect a specific way of governing in the information society era throught the technoscientific transformation of life, which is a political debate.
Rousseau, Olivier. "Accélération de l'exploration de l'espace chimique du cytochrome P450 BM3 par des méthodes de criblage à haut débit et bio-informatiques". Thèse, 2018. http://hdl.handle.net/1866/21949.
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